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Abstract:

An active medical device able to discriminate between tachycardias of
ventricular origin and of supra-ventricular origin. Two distinct temporal
components (UnipV, BipV) are obtained corresponding to two EGM signals of
ventricular electrograms. The diagnosis operates in at least
two-dimensional space to determine, from the variations of one temporal
component as a function of the other temporal component, a 2D
characteristic representative of a heart beat and, this, for a reference
beat collected in Sinus Rhythm (SR) in the absence of tachycardia
episodes, and for a heart beat in Tachycardia. The discrimination of the
tachycardia type, VT or SVT, is then realized by a classifier operating a
comparison of the two current and reference 2D characteristics.

Claims:

1. An active medical device, having means for collecting an electrical
activity of a patient's heart, including means for producing at least two
distinct temporal components corresponding to two EGM signals of a
ventricular electrogram; means for detecting a presence of a tachycardia
episode in said collected electrical activity; and diagnosis means for
diagnosing a ventricular tachyarrhythmia including means for
discriminating in the detected tachycardias between a Ventricular
Tachycardia originated in the ventricles and a Supra-Ventricular
Tachycardia originated above the ventricles, wherein the improvement
comprises:the collecting means further comprising means for producing a
first and a second distinct temporal components from said two distinct
EGM signals of ventricular electrogram;the diagnosis means further
comprises means for conducting a bi-dimensional analysis, able to
determine, from the variations of one of said first and second temporal
components as a function of the other of said first and second temporal
components, a 2D characteristic representative of a heart beat;
anddiscriminator means further comprising means for comparing:a first
current 2D characteristic, representative of a tachycardia beat (SVT,
VT), from said at least two EGM signals collected during a tachycardia
episode, witha second reference 2D characteristic, representative of a
Sinus Rhythm (SR) beat from said two EGM signals, collected from
non-tachycardia episodes.

2. The device of claim 1, wherein the EGM signals include a QRS complex
and the means for conducting the bi-dimensional analysis further
comprises a temporal window (WQRS) including a QRS complex of said
heart beat and means for determining said 2D characteristic based on said
temporal components occurring during said window.

3. The device of claim 1, wherein the diagnosis means further comprises
means for determining said reference 2D characteristic from a plurality
of averaged successive heart beats.

4. The device of claim 3, wherein the diagnosis means further comprises
means for detecting not representative beats in said plurality of heart
beats, and excluding said not representative beats from the determination
of the reference 2D characteristic.

5. The device of claim 4, wherein the means for detecting the
non-representative heart beats further comprises means for performing a
morphological analysis of said plurality of heart beats by a
cross-correlation.

6. The device of claim 5, wherein the means for performing a morphological
analysis further comprises means for identifying by clustering of
non-representative beats.

7. The device of claim 1, wherein the discriminator means further
comprises means for charactering said current and reference 2D
characteristics by at least one geometrical descriptor ({right arrow over
(e)}T, c), and comparing said current and reference 2D
characteristics by the determined geometrical descriptor.

8. The device of claim 7, wherein the geometrical descriptor further
comprises a unit tangent vector ({right arrow over (e)}T) to the 2D
characteristic, considered at a plurality of points.

9. The device of claim 8, wherein the discriminator means further
comprises means for evaluating an average angle between unit tangent
vectors to respectively the current 2D characteristic and the reference
2D characteristic.

10. The device of claim 7, wherein said geometrical descriptor further
comprises a norm of the velocity vector of the 2D characteristic,
considered in a plurality of points.

11. The device of claim 10, wherein the discriminator means further
comprises means for evaluating a correlation coefficient between the
norms of the velocity vectors of respectively the current 2D
characteristic and the reference 2D characteristic.

12. The device of claim 7, wherein said geometrical descriptor further
comprises the curvature (c) of the 2D characteristic, considered in a
plurality of points.

13. The device of claim 12, wherein the discriminator means further
comprises means for evaluating a correlation coefficient between the
respective curvatures of the current 2D characteristic and the reference
2D characteristic.

14. The device of claim 1, wherein the bi-dimensional analysis means
further comprises means for determining a reference mark orthonormal to
an axis corresponding to a main axis of the patient's heart.

15. The device of claim 14, wherein means for determining said reference
mark further comprises means for analyzing a sinus EGM signal collected
in the absence of tachycardia episodes.

16. The device of claim 14, further comprising means for applying to said
first and second 2D characteristics a reference change, from a primitive
reference to said reference mark.

17. The device of claim 16, wherein said diagnosis means further comprises
means for analyzing said main axis components and producing a first
descriptor parameters of a morphology of said first and second 2D
characteristics.

18. The device of claim 17, wherein said first descriptor parameters are
parameters selected from among the group consisting of: first and second
eigen values of a covariance matrix associated to each of these eigen
values; orientation of the main axis and a secondary axis; a ratio
between the extreme signal amplitudes on each of the channels; and an
area circumscribed by the 2D characteristic.

19. The device of claim 14, wherein said diagnosis means further comprises
means for producing a first and a second one-dimensional component by
projection of each of said first and second 2D characteristic on one of
the axis of said reference mark.

20. The device of claim 19, wherein said diagnosis means further comprises
means for producing second descriptor parameters of the morphology of
said first and second one-dimensional component.

21. The device of claim 20, wherein said second descriptor parameters are
parameters selected from among the group consisting of: a signal maximum
height; a signal minimum height; and a signal width.

22. The device of claim 14, wherein said diagnosis means further comprises
means for providing an inter-correlation between said first and second 2D
characteristics.

23. The device of claim 1, wherein the bi-dimensional analysis means
further comprises:means for determining a reference mark orthonormal to
an axis corresponding to a main axis of the patient's heart;means for
applying to said first and second 2D characteristics a reference change,
from a primitive reference to said reference mark;means for analyzing
said main axis components and producing a first descriptor parameters of
a morphology of said first and second 2D characteristics, wherein said
first descriptor parameters are first and second eigen values of a
covariance matrix associated to each of these eigen values; andmeans for
providing an inter-correlation between said first and second 2D
characteristics, wherein said inter-correlation means further comprises
means for providing a bi-dimensional distribution analysis between the
correlation coefficients and the eigen values of the covariance matrix of
an analysis in principal components.

24. The device of claim 23, wherein said inter-correlation means further
comprises means for providing a three-dimensional distribution analysis,
able to define, for at least one descriptor parameter of the morphology
of said first and second 2D characteristics, a discriminator plane
between ventricular originated tachycardias and supra-ventricular
originated tachycardias.

25. The device of claim 24, wherein said inter-correlation means further
comprises a linear classifier means for performing said three-dimensional
distribution analysis.

27. The device of claim 14, wherein diagnosis means are essentially devoid
of an analysis in principle components.

28. The device of claim 27, wherein said diagnosis means further comprises
means for determining ratios between a maximum amplitude and a minimum
amplitude of a depolarization complex for each of said two distinct
temporal components, respectively for said Sinus Rhythm and tachycardia
beats.

29. The device of claim 27, wherein said diagnosis means further comprises
means for determining a correlation maximum between said 2D
characteristics from said Sinus Rhythm and Tachycardia beats.

Description:

FIELD OF THE INVENTION

[0001]The present invention relates to analyzing ventricular
tachyarrhythmias, more preferably to active implantable medical devices
(according to the directive 90/385/CEE dated Jun. 20, 1990) performing
such analyses, and more particularly to such implantable devices that are
able to apply to the heart therapies requiring the delivery of
controlled, high energy electrical stimulation pulses that are designed
to terminate a tachyarrhythmia and/or deliver high frequency pacing
therapies known as ATP (AntiTachycardia Pacing). It should be understood,
however, that the invention can be implemented not only in an implant,
but also externally to the patient, for example, in an external
programmer used by a physician to download and analyze the cardiac
signals collected and memorized by the implant. The invention can also be
implemented in a so-called "home monitoring" monitor, which is a
particular type of programmer the functioning of which is entirely
automated; a physician is not required with such a home monitoring
monitor and this equipment can notably remotely transmit at regular or
defined intervals to a distant site data collected by an implant for
analysis and physician follow-up of the patient. In addition or in the
alternative, the present invention can also be implemented at the data
server level to operate on the rough patient data transmitted by the
patient's home monitor.

BACKGROUND OF THE INVENTION

[0002]A tachyarrhythmia (also called a tachycardia) is generally an
abnormal rapid cardiac rhythm that can be from a sinus, atrial or
ventricular origin. More specifically, a tachycardia can encompass
several varieties of cardiac rhythm disorders: when a tachyarrhythmia is
present, its origin can be a ventricular fibrillation (VF), a sinus
tachycardia (ST) or a Supra-Ventricular Tachycardia (SVT). The SVT
includes the atrial tachycardia, the atrial flutter and the atrial
fibrillation (AF). Those disorders can exist simultaneously and in that
case, the patient suffers from "bi-tachycardia", notably in the presence
of an atrial fibrillation combined with a Ventricular Tachycardia.

[0003]But it is not always that simple to determine the origin of an
existing tachycardia. In the case of a device able to deliver a therapy
such as a defibrillation shock, such a shock should only be delivered in
case of a real Ventricular Tachycardia (VT) and not in the case of a
Supra-Ventricular Tachycardia (SVT). Indeed, in case of SVT, the
tachycardia is originated from the atrium and any shock that would be
delivered would have no beneficial or therapeutic effect, because the
defibrillation electrode is not implanted in the atrial area.

[0004]Further, the application of a defibrillation shock in a conscious
patient is extremely nerve-racking and painful, indeed the energies
applied are far above the pain threshold. In addition, delivering a
defibrillation shock has adverse effects on the cardiac rhythm (risks of
secondary troubles), on the functional integrity of the myocardium and,
in a general way, on the physiological equilibrium of the patient.
Therefore, it is desirable and important to deliver only appropriate
shocks and only a defibrillation shock if a less painful therapy, such as
an appropriate pacing of the atrium, can not be successfully applied.

[0005]One problem with tachycardias comes from the recognition that, in a
number of pathologic cases, certain events are present, but not visible,
because they are masked by other simultaneous events. For example, the
wide rapid VT complex makes it difficult to recognize P waves, which does
not always allow to differentiate them from a flutter associated to a
functional bundle branch block. There is, therefore, a need to be able to
recognize these masked phenomena and, in particular, the P waves, in this
field.

[0006]But, if it is difficult for the physician, it is more difficult for
automated cardiac rhythm analysis systems to make this discrimination.
The discrimination criteria used in these automated devices include, in
particular, the stability of the ventricular intervals (RR intervals),
the analysis of the atrioventricular association (characterized by the
stability of the PR interval) and the starting mode of the tachycardias
(presence of a sudden acceleration and the cavity of origin, ventricular
or atrial).

[0007]It is known from EP 0 626 182 A1, and its counterpart U.S. Pat. No.
5,462,060 (assigned to ELA Medical), to employ a tachyarrhythmia
detection and classification algorithm named PARAD/PARAD+, implemented in
particular in the Defender and Ovatio brand ELA Medical devices. Further,
EP 0 838 235 A1 and its corresponding U.S. Pat. No. 5,868,793, and EP 0
813 888 A1 and its corresponding U.S. Pat. No. 5,891,170, and EP 1 208
873 A1 and its corresponding U.S. Pat. No. 6,889,080 (all three assigned
to ELA Medical) describe various improvements of this algorithm, allowing
to improve again the discrimination between Ventricular Tachycardia and
Supra-Ventricular Tachycardia, notably to avoid a false positive
diagnosis (indication of a Ventricular Tachycardia when the disorder is a
Supra-Ventricular Tachycardia) or a false negative diagnosis (indication
of a Supra-Ventricular Tachycardia when the disorder is a Ventricular
Tachycardia).

[0008]Other proposals have also been made to discriminate between
Ventricular Tachycardia and Supra-Ventricular Tachycardia, based on a
morphologic analysis of the QRS complex alone, hence without using the P
wave that is difficult to recognize. Those techniques based on a
morphological analysis of the QRS are the more often used by
cardiologists in clinical practice, when they analyze an ECG diagram to
characterize the ventricular arrhythmias, which are generally the more
threatening ones.

[0009]But the application of such methods to automated detection
algorithms embedded in implanted cardiac prosthesis is not considered
reliable enough, in part because the potential information contained in
the endocardial electrogram signals (EGM), collected by these devices, is
not completely controlled and is less controlled than the ECG signals
collected by an external recorder. In particular, the normality
parameters of these signals are widely unknown, which does not allow
discriminating by comparison between the pathological situations and the
others.

[0010]In addition, the analysis algorithms are complex and, often, require
incompatible requirements, in terms of calculation (computing) power and
energy consumption, for a miniaturized implanted device. This leads to
propose sub-optimal solutions based on algorithms, which do not allow a
sufficiently reliable diagnosis.

[0011]Various algorithms for implantable defibrillators, based on a
morphological analysis, are known to exist. These algorithms implement
methods based on the following property: during a Supra-Ventricular
Tachycardia episode, the electrical pulses are conducted in the
ventricles by the same conduction paths as in Sinus Rhythm, so that the
morphology of the ventricular contraction signal is very similar to that
of the signal recorded in Sinus Rhythm. On the other hand, during a
Ventricular Tachycardia episode, the conduction paths are different, and
the recorded electrical signal is different. Hence, those known methods
propose to discriminate VT/SVT by the measurement of the similarity of
the recorded signals during the arrhythmia with the recorded signals in
Sinus Rhythm.

[0012]US 2005/0159781 A1 (Cardiac Pacemakers, Inc.) describes a technique
named "VTC" (electrogram Vector Timing and Correlation), in which the
algorithm analyses the amplitude and the temporal position of a certain
number of singular points, representative of a QRS complex collected on
an endocardial EGM channel, typically on the right ventricle (RV). Before
this, the algorithm creates a Sinus Rhythm reference beat, by: (i)
collecting a certain number of complexes from a unipolar RV signal
(between the can (e.g., the case of the implant) and an electrode on the
lead), (ii) aligning these complexes by the use of a corresponding
bipolar RV signal (collected between two electrodes on the lead), (iii)
calculating an average value of the complexes aligned in this manner and,
finally, (iv) extracting from the average reference beat eight
representative points (minimum, maximum, inflection point . . . ) to
define a model or "template". After that, when an arrhythmia is detected,
the VTC algorithm calculates the correlation coefficient between these
eight reference points from the model and the eight analog points from
each tachycardia beat collected on the (one) unipolar RV signal channel.
If, for a given tachycardia, the algorithm identifies a sufficiently high
number of non correlated beats, then the tachycardia is classified as
being of a ventricular origin--which can then justify the application of
a defibrillation shock. In the case of a dual chamber defibrillator, the
VTC morphological analysis algorithm can be improved, by taking into
account additional non morphological criteria (V>A and stability).

[0013]Another method, named "MD" (Morphology Discrimination) and described
in U.S. Pat. No. 7,149,569 B1 (Pacesetter Inc.), uses an algorithm which
intends to calculate a matching percentage below a model beat and each
beat of the arrhythmia to be analyzed, this percentage being a function
of the amplitude, of the polarity and of the order of the peaks. If at
least five beats among eight have a matching percentage below a threshold
value, then the arrhythmia is characterized as being a tachycardia
originated from the ventricle (the threshold can be programmed with
values comprised between 30% and 95%). The clinical studies nevertheless
show that this algorithm must be programmed so that it also takes into
account non morphological criteria (acceleration, stability), so as to
provide satisfactory results.

[0014]WO 00/69517 A1 (Medtronic Inc.) describes a third method, named
Wavelet Dynamic Discrimination, which concerns comparing the morphology
of a basic rhythm and the morphology of the tachycardia, based on the
difference between wavelet coefficients, this difference being a matching
percentage. The beats for which this percentage is below 70% are
classified as originated from the ventricle, after which a tachycardia is
classified as being originated from the ventricle if at least six beats
out of eight fulfill this criteria.

[0015]All in all, whatever the implemented technique, until now, the
proposed algorithms all are exposed to being deluded in certain
particular clinical situations, and resulting in, as a consequence, a
wrong Ventricular Tachycardia diagnosis and, so, the risk of delivering
an inappropriate therapy.

OBJECTS AND SUMMARY OF THE INVENTION

[0016]It is, therefore, an object of the present invention to overcome the
above-referenced drawbacks, by proposing an improved analysis technique
that minimizes the risk of false VT diagnosis (false positive or false
negative) during the discrimination between VT and SVT, hence to reduce
the number of inappropriate shocks due to a wrong discrimination, and
consequently ensuring a greater reliability in the tachyarrhythmia
analysis.

[0017]In other words, the object of the present invention is to improve
the decision-making of an implantable defibrillator in the discrimination
between VT and SVT, by improving the specificity while maintaining the
sensitivity.

[0018]Broadly, the present invention is based on the assessment that all
the relevant parameters to discriminate between a VT and a SVT can be
obtained by analyzing EGM signals originated from the same cavity (e.g.,
the ventricle) collected simultaneously on two distinctive channels,
those signals being combined in the form of two respective components
applied to a bi-directional analysis--which means without taking directly
into account time dimension. The two different EGM channels are, for
example, one from a unipolar signal (collected between the can and one of
the distal and the proximal electrodes), and one from a bipolar signal
(collected between the distal and proximal electrodes).

[0019]It should be understood that the present invention is not limited to
a "bi-dimensional" analysis or an analysis "in two dimensions" (2D as
discussed in detail herein), but rather that these are illustrative
embodiments and indeed the invention applies also in a multi-dimensional
space (3D or more), by extrapolation of the teachings of the present
description to a situation where the EGM signals from a same cavity are
collected simultaneously on three or more channels.

[0020]The invention proposes, as with the prior known methods, to perform
the VT/SVT discrimination based on a measure of the matching of recorded
signals during the arrhythmia with those recorded in Sinus Rhythm.
Advantageously, in a characteristic manner of the present invention this
VT/SVT discrimination is performed using a "cardiac loop" or "vectogram",
which is the representation of one of those signals as a function of the
other, in a two-dimensional space. This space is typically a "unipolar
channel (in ordinate) versus bipolar channel (in abscissa)". Each current
heart beat (or optionally each significant fraction of a heart beat) is
then represented by its vectogram in the plane so defined. In case of
arrhythmia, the current heart beat is compared to a reference vectogram,
collected in Sinus Rhythm. The algorithm estimates the similarity between
the current and the reference vectograms and, consequently, discriminates
the arrhythmia type, VT (low similarity) or SVT (high similarity).

[0021]Broadly, the present invention proposes an improvement to an active
medical device of the type described in US 2005/0159781 A1. One aspect of
the present invention is directed to an active medical device, having
circuits and control logic signal processing for collecting an electrical
activity of a patient's heart and producing at least two distinct
temporal components corresponding to two EGM signals of a ventricular
electrogram and that is able to detect a presence of a tachycardia
episode in the collected electrical activity, diagnose a ventricular
tachyarrhythmia, and discriminate in the detected tachyarrhythmia between
a Ventricular Tachycardia originated in the ventricle and a
Supra-Ventricular Tachycardia, wherein the improvement comprises:

[0022]producing a first and a second distinct temporal component from two
distinct EGM signals of a ventricular electrogram;

[0023]conducting a bi-dimensional analysis, able to determine, from the
variations of one of said first and second temporal components as a
function of the other of said first and second temporal components, a 2D
characteristic representative of a heart beat; and

[0024]discriminating between a Ventricular Tachycardia and a
Supra-Ventricular Tachycardia by comparing:

[0025]a first current 2D characteristic, representative of a tachycardia
beat (SVT, VT), from said two EGM signals collected during a tachycardia
episode, with

[0026]a second reference 2D characteristic, representative of a Sinus
Rhythm (SR) heart beat from said two EGM signals.

[0027]Preferably, the bi-dimensional analysis is conducted using a
temporal window (W) including the QRS complex of the cardiac beat and
determining the 2D characteristic based on said temporal components
occurring during said window W.

[0028]In one embodiment, the diagnosis determines the reference 2D
characteristic from a plurality of averaged successive heart beats. More
preferably, in the diagnosis "non-representative" beats in that plurality
of cardiac beats are detected and excluded from the determination of the
reference 2D characteristic. The detection of the non-representative
beats can be obtained by performing a morphological analysis of the
plurality of cardiac beats by a cross-correlation, for example, by
identifying by clustering of the representative beats.

[0029]In yet another embodiment, the discrimination of tachycardias is
performed by charactering the current and reference 2D characteristics by
at least one geometrical descriptor ({right arrow over (e)}'T c),
and comparing the current and reference 2D characteristics by the
determined geometrical descriptor. The geometrical descriptor is, for
example, the unit tangent vector ({right arrow over (e)}T) to the 2D
characteristic, also called normalized velocity vector, considered at a
plurality of points. More preferably, this discrimination involves
evaluating an average angle between unit tangent vectors to respectively
the current 2D characteristic and the reference 2D characteristic.
Alternatively, the geometrical descriptor can be the norm of the velocity
vector of the 2D characteristic, considered in a plurality of points.
More preferably, this discrimination involves evaluating a correlation
coefficient between the norms of the velocity vectors of respectively the
current 2D characteristic and the reference 2D characteristic. Finally,
the geometrical descriptor can be the curvature (c) of the 2D
characteristic, considered in a plurality of points, and more preferably,
the discrimination involves evaluating a correlation coefficient between
the respective curvatures of the current 2D characteristic and the
reference 2D characteristic.

[0030]An alternate preferred embodiment employs a bi-dimensional analysis
in which is determined a reference mark orthonormal to an axis
corresponding to a main axis of the patient's heart. The reference mark
is preferably determined by analyzing a sinus EGM signal collected in the
absence of tachycardia episodes. Also, a reference change, from a
primitive reference to that reference mark, can be applied to the first
and second 2D characteristics.

[0031]In this embodiment, the diagnosis can include analyzing the main
axis components and producing first descriptor parameters of the
morphology of said first and second 2D characteristics. The first
descriptor parameters may be selected from among the group consisting of:
first and second eigen values of a covariance matrix associated to each
of these eigen values; orientation of the main and secondary axis; a
ratio between the extreme signal amplitudes on each of the channels; and
an area circumscribed by the 2D characteristic.

[0032]In an alternate embodiment, the diagnosis can include producing a
first and a second one-dimensional component by projection of each of the
first and second 2D characteristics on the axis of the reference mark.
The diagnosis in this case can produce second descriptor parameters of
the morphology of said first and second one-dimensional components. The
second descriptor parameters are selected from among the group consisting
of: a signal maximum height; a signal minimum height; and a signal width.

[0033]In a still further variation, the diagnosis can be performed by
means for providing an inter-correlation between said first and second 2D
characteristics. The inter-correlation provides a bi-dimensional
distribution analysis between the correlation coefficients and the eigen
values of the covariance matrix of an analysis in main components.

[0034]Alternatively, the inter-correlation may be employed to provide a
three-dimensional distribution analysis, able to define, for at least one
descriptor parameter of the morphology of the first and second 2D
characteristics, a discriminator plane between ventricular originated
tachycardias and supra-ventricular originated tachycardias. The
inter-correlation, can use a linear classifier or an adaptive neural
network classifier for performing said three-dimensional distribution
analysis.

[0035]In yet another embodiment, the diagnosis is performed so as to be
essentially devoid of any analysis in main components. This can be
achieved, for example, by determining ratios between a maximum amplitude
and a minimum amplitude of a depolarization complex for each of said two
distinct temporal components, respectively for said Sinus Rhythm and
tachycardia beats, or by determining some correlation maximum between
said 2D characteristics from said Sinus Rhythm and tachycardia heart
beats.

BRIEF DESCRIPTION OF DRAWINGS

[0036]Further features, advantages and characteristics of the present
invention will now be described in connection with the following detailed
description of preferred embodiments of the present invention, made with
reference to the attached drawings in which the same numerical references
designate identical or functionally similar elements, and in which:

[0039]FIG. 3 illustrates the cardiac loops collected by combining the two
signals of the FIGS. 1 and 2 for a same patient, in Sinus Rhythm and
during a Supra-Ventricular Tachycardia episode;

[0040]FIG. 4 illustrates the cardiac loops collected by combining two
signals, for a same patient in Sinus Rhythm and during a Ventricular
Tachycardia episode, analogously to FIG. 3;

[0041]FIG. 5 illustrates a first embodiment of the invention in which
electrogram signals typically collected on the ventricular bipolar and
ventricular unipolar channels are simultaneously recorded for a given
patient;

[0042]FIG. 6 illustrates a vectogram collected by combining the two
signals of FIG. 5, for eight successive beats;

[0043]FIG. 7 is a flow-chart illustrating a process for a reference beat
estimation algorithm in Sinus Rhythm;

[0044]FIGS. 8 and 9 illustrate how the correlation between the beats is
analyzed, designated to discriminate between a heart beat in Sinus Rhythm
and premature ventricular contractions;

[0045]FIG. 10 illustrates two parameters of characterization of a
vectogram in a given point, namely the radius of curvature and the
tangent vector at that point;

[0046]FIG. 11 is a flow-chart illustrating a process for the morphological
classification algorithm designed to determine the nature, ventricular or
supra-ventricular, of a detected tachycardia in a patient;

[0047]FIGS. 12a-12j graphically illustrate the different parameters
calculated by the characterization algorithm for a same patient,
respectively in Sinus Rhythm and during a Supra-Ventricular Tachycardia
episode, as well as a method to analyze these parameters in order to
deduce the nature of this tachycardia;

[0048]FIGS. 13a-13j are homolog to what is illustrated in FIGS. 12a-12j
for a patient in Sinus Rhythm but during a Ventricular Tachycardia
episode;

[0049]FIG. 14 is a synopsis flow chart showing the method by which the
morphological analysis according to a preferred embodiment of the present
invention can be combined to a rhythm analysis to improve the specificity
of an existing device;

[0050]FIG. 15 illustrates, for a second embodiment of the present
invention, collected cardiac loops, in the case of a Sinus Rhythm and in
the case of a Supra-Ventricular Tachycardia respectively, when the
components of these loops are projected in the base defined by the Sinus
Rhythm;

[0051]FIG. 16 is homolog to FIG. 15 for a Sinus Rhythm, but for a
Ventricular Tachycardia;

[0052]FIG. 17 illustrates the variations of the signals corresponding to a
Sinus Rhythm and to a Ventricular Tachycardia, when these signals are
projected on the main axis and on the secondary axis of a cardiac loop,
said axis being determined by an analysis in principal components,
according to the second mode of implementation of the invention; and

[0053]FIG. 18 illustrates a technique allowing, after a correlation
analysis, to discriminate between Ventricular Tachycardia and
Supra-Ventricular Tachycardia made from the correlation results,
according to a second embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[0054]With reference to the drawings, two preferred embodiments of the
present invention will be hereinafter described, applied to an active
implantable medical device, allowing to continuously monitor the cardiac
rhythm and deliver to the heart, as necessary, in case of rhythm trouble
detected by said implant, electrical stimulation pulses for
resynchronization and/or defibrillation.

[0055]As regards the software aspects, the present invention can be
implemented by an appropriate programming of the control software of a
known device, for example, a device of the cardiac stimulation,
resynchronization, or defibrillator type, such device having suitable
circuitry to acquire a signal provided by the endocardial leads.

[0056]The invention can preferably be applied to implantable devices such
as the devices of the Ovatio brand commercialised by ELA Medical,
Montrouge, France. These are devices having a programmable microprocessor
to which it is possible to transmit by telemetry software applications
which will be downloaded and embedded in associated memory and executed
to implement the functions of the invention as described herein. The
adaptation of these known implantable devices to the implementation of
the functions of the present invention is believed to be within the
abilities of a person of ordinary skill in the art and, therefore, will
not be described in detail.

[0057]As described above, a preferred embodiment of the present invention
provides an analysis technique for operating a discrimination between
Ventricular Tachycardia (VT) and Supra-Ventricular Tachycardia (SVT) from
the EGM electrogram signals collected on two distinctive channels and
analyzed in two dimensions.

[0058]With reference to FIG. 1, in the case of a patient with a Sinus
Rhythm (SR) the BipV and UnipV electrogram are illustrated, observed
respectively on the ventricular bipolar (FIG. 1a) and on the ventricular
unipolar (FIG. 1b) channels. With reference to FIG. 2, in the same
manner, the BipV and UnipV corresponding signals are illustrated in the
case of a patient in SVT. Those signals are subjected to an appropriate
filtering, normalizing and centering pre-processing (this pre-processing
signal conditioning, classical in itself, forms no part of the present
invention and therefore is not described in detail).

[0059]Once these signals are collected (in the time domain), the next step
is tracing one of the signals as a function of the other. The created
characteristic, named a "cardiac loop", is illustrated in FIG. 3, on one
hand in the case of a Sinus Rhythm (loop in continuous line) and on the
other hand in the case of a SVT (loop in dotted line), in the same
patient. Each of these loops is representative of a complete heart beat,
either in Sinus Rhythm, or in SVT. It should be understood, however, as
described below, that it is not mandatory to analyze the complete heart
beat, and that the analysis of a significant portion of this heart beat
(typically, a portion centered on or about the QRS complex) is generally
sufficient to operate the expected discrimination.

[0060]By comparing the two cardiac loops illustrated in FIG. 3, it can be
pointed out that the Sinus Rhythm loop (corresponding to a beat in Sinus
Rhythm) and the SVT loop (corresponding to a beat in Supra-Ventricular
Tachycardia) have some similarities in terms of loop direction, loop
orientation, propagation direction, as well as their shape and the
circumscribed area.

[0061]On the other hand, with reference to FIG. 4, the collected loops
illustrated in the case of a patient in Sinus Rhythm (continuous line
loop) with episodes of VT (dotted line loop), the VT loop is
significantly different than the one collected in SR, and it can be
pointed out there is little or no similarity.

[0062]The invention mainly proposes to systematize this approach by
analyzing the 2D vectogram characteristics by comparison to a
corresponding reference vectogram, collected in a Sinus Rhythm.

First Embodiment of the Present Invention

[0063]An analysis method and apparatus in accordance with a first
embodiment of the present invention, will now be described with reference
to FIGS. 5 to 14. In this embodiment, after each detection of a bipolar
signal depolarization peak BipV (corresponding to a detected R wave), the
corresponding beat is isolated by a permanent window WQRS having a
duration of several tens of milliseconds centered on the determined
depolarization peak, for example, a width window WQRS=80 ms
corresponding to 80 points for a sampling frequency of 1000 Hz. This
typical 80 ms value allows to appropriately isolate the QRS complex to
analyze its morphology, without inducing too much noise around, said
noise corresponding to the base line after the end of the QRS.

[0064]The device keeps stored in memory a plurality of successive beats,
for example, the last eight beats B1 to B8, as illustrated in FIG. 5,
those beats being recorded simultaneously on the ventricular bipolar
channel (BipV) and on the ventricular unipolar channel (UnipV). The
portion of each of these beats comprised inside the window WQRS is
then represented by a vectogram, considered in the plane formed by the
bipolar channel in abscissa and by the unipolar channel in ordinate. It
shall be pointed out that the vectogram corresponding to each of these
beats is not a closed loop, because it corresponds only to a portion of
the complete cardiac loop, that portion being the QRS complex isolated
inside the WQRS window.

[0065]The analysis requires the creation of a reference beat, preferably
averaged from a succession of beats in Sinus Rhythm, to be used in the
discrimination.

[0066]However, it is necessary, even in the absence of a tachycardia, to
exclude certain non-significant beats: on the drawn vectograms
illustrated in FIG. 6, it can be noticed that two among the eight
vectograms VG1 . . . VG8 have a significantly different form
than the other six: they correspond to premature ventricular conductions
or PVC (specifically beats B4 and B8 on FIG. 5). Such beats must be
identified and excluded from the calculation of the average reference
beat, because their morphology is in no way representative.

[0067]Referring to FIG. 7, a Sinus Rhythm analysis and determination of a
representative reference beat algorithm is illustrated. For each
detection of an R wave on the bipolar channel (step 10), the device
stores the eight successive waves (step 12) and isolates the QRS complex
in the window WQRS for each of the collected beat on the bipolar
channel (step 14).

[0068]In the absence of a tachycardia (tested at step 16), the algorithm
determines whether it is required or not to create or update the
reference beat (step 18). Indeed, even if there is still a reference
heart beat, it can be desirable to recalculate it on a regular basis
(typically at least once a day, or at least every hour after the implant
so as to take into account the electrode maturation phenomenon after the
lead implant), and/or according to the status of the patient
(rest/exercise . . . ).

[0069]When it is required to create or recalculate the reference heart
beat, the algorithm selects the representative beats among the eight
memorized beats, by isolating and removing the PVC and the various
artifacts such as improperly centered windows. A first simple method to
select the representative beats concerns keeping only the complexes for
which the RR intervals are stable, and to average point by point the
complexes fulfilling these criteria. Another method, illustrated with
reference to FIGS. 8 and 9, concerns analyzing the morphology of the
eight beats by cross-correlation. To that purpose, a beat is randomly
selected as the reference, for example, the fourth of the eight beats
illustrated in FIG. 5 (which is a PVC). A correlation coefficient is
calculated between this reference beat and each of the seven other beats,
for both the bipolar signal and the unipolar signal. The corresponding
correlation coefficients C.sub.BipV (i,4) and C.sub.UnipV (1,4) of each
beat Bi can then be represented in a plane by a point which abscissa is
C.sub.BipV(i,4) and which ordinate is C.sub.UnipV (i,4), the point
corresponding to the fourth beat (i=4) being the point (1,1).

[0070]If all the correlation coefficients are higher than 0.9, then the
reference beat in slow rhythm is calculated by averaging point by point
the eight beats, this being performed for each of the bipolar and
unipolar channels (step 22 and 22' on FIG. 7). On the other hand, if
there are values below 0.9 (as in the case of the illustrating example),
then an iterative algorithm of unsupervised clustering is applied to
these eight points, for example, a K-means algorithm. Such an algorithm,
in itself well-known, portions the data in K homogeneous classes,
minimizing the intra-class variance so as to obtain, in an iterative
method, some clusters based on the Euclidian distance between the points.
Referring to FIG. 8, it can be noticed that the points can be brought
together in two clusters, in the upper right and lower left regions of
the plot. For each point, if its distance to the center of the cluster is
greater than half of the distance between the two respective clusters,
than it will be considered that this point does not belong to any
cluster. (Note that this is not the case in the example illustrated on
FIG. 9, where the intra-cluster distance is notably below half of the
inter-cluster distance). Finally, the algorithm selects the cluster which
contains the most elements, which is the lower left cluster on FIG. 8.

[0071]The reference beat in slow rhythm is calculated on each of the two
bipolar and unipolar channels (steps 22 and 22') by averaging point by
point the beats corresponding to the selected clusters: in the example,
the vectograms referenced as "Sinus Rhythm" in FIG. 9, corresponding to
the "Sinus Rhythm" clusters of FIG. 8, will be averaged point by point to
get the reference beat, whereas the two vectograms referenced as "PVC" on
FIG. 9, corresponding to the beats B4 and B8 of the "PVC" cluster of FIG.
8, will be eliminated, because they correspond to PVC (or to artifacts).

[0072]From these point by point average values of the beats on the bipolar
and unipolar channels, the algorithm then determines a vectogram of the
reference beat (step 24, of FIG. 7), by representing in abscissa the
variations of the bipolar channel and in ordinate the variations of the
unipolar channel, for each of the sampling points of the signals inside
the window W. This vectogram is then characterized in each of its points.

[0073]The invention proposes, for example, to realize this
characterization by two descriptors: the unit tangent vector {right arrow
over (e)}T and the curvature c (which is the inverse of the radius
of curvature r) at the point P of the reference vectogram VGREF, and
this for the successive different sampled points of the vectogram (steps
26 and 26', of FIG. 7). Another possible descriptor is the norm of the
velocity vector.

[0074]The unit tangent vector {right arrow over (e)}T at a given
point can be determined by a known technique, preferably with a discrete
filter which approximates the first derivatives, for example, on four
points for a sampling frequency of 1000 Hz. This filtering is then
typically followed by a normalization (so that the tangent vector is
unitary).

[0075]The curvature c can be calculated in a given point of the vectogram
from the first derivatives and from the second derivatives, preferably
calculated with the same method as for first derivatives. Favourably, to
give more importance to the interesting zones of the vectogram where the
points are the more distant, the curvature is then weighted by a power of
the distance between the points. This distance is calculated from a
discrete filter applied to the Euclidian distances in the vectogram space
between two successive points. Finally, the curvature is normalized.

[0076]The reference vectogram has then been determined and characterized
by its tangent vector and its curvature in each point.

[0077]In the case of a tachycardia, the device will then be able to
determine the nature of this tachycardia by a morphological analysis
involving a comparison with the reference vectogram as defined.

[0078]The general tachycardia classification algorithm is illustrated on
FIG. 11. The device detects and memorizes the eight last beats, more
preferably by keeping the only information centered on a window WQRS
around the bipolar signal depolarization peak (steps 30, 32 and 34). The
method to be used is the same as the one described above with reference
to FIG. 5, the steps 30, 32 and 34 being similar to the steps 10, 12 and
14 previously described.

[0079]The algorithm can eventually decide to continue the morphological
analysis on the basis of the existence of a VT previously confirmed by
the rhythm analysis (test 36); for example, by the known algorithms such
as PARAD, PARAD+ or STABILITY+ as implemented in the above-described ELA
Medical devices and described in the above-cited documents EP 0 626 182
A1 and corresponding U.S. Pat. No. 5,462,060 (ELA Medical) and others.
The combination of the rhythm analysis and of the morphological analysis
will be described hereafter with reference to FIG. 14, nevertheless, it
can be pointed out that a prior detection of a VT by the rhythm analysis,
before the morphological analysis is performed, is not a necessary
characteristic to implement the invention and, consequently, the step 36
is an optional step.

[0080]The next step (step 38) concerns drawing the vectograms of the last
eight beats and to characterize them in each of their points by the two
descriptors (unit tangent vector and weighted and normalized curvature).

[0081]The comparison between a vectogram collected in tachycardia with the
reference vectogram collected in Sinus Rhythm for the same patient is
performed by the calculation of two quantities:

[0082]the average angle α between the unit tangent vectors of both
respective vectograms and

[0083]the correlation coefficient cc between the curvature of the two
respective vectograms.

[0084]The discrimination between VT and SVT will be operated on the values
of α and of cc, for example, by comparison with the previously
determined decision thresholds from a learning base. Thus:

[0085]if the average angle α is below a given value (steps 40 and
42), or if the correlation coefficient cc is higher than a given
threshold depending of the heart rate (steps 46 and 48), then the beat
corresponding to the arrhythmia is classified as being from a
supra-ventricular origin (step 44);

[0086]otherwise, it is classified as being from a ventricular origin (step
50).

[0087]The correlation coefficient threshold corresponds to a heart rate
quadratic function, this function being calculated on the complete
training set by classical methods of supervised classification, such as
the least square method. The supervised classification concerns
establishing from a sample of classified data a decision frontier
separating the two classes by minimizing the square error (as defined in
the least square method) between the true values (for example, +1 for VT
and -1 for SVT) and the values predicted by the classifier.

[0088]The next step (step 52) compares the results collected for each of
the eight successive beats:

[0089]if at least six of the eight beats are classified as being from a
ventricular origin, the arrhythmia, at this stage, is classified as being
from a ventricular origin, according to the morphological analysis (step
54);

[0090]if at least six of the last eight beats are classified as being from
a supra-ventricular origin, the arrhythmia, at this stage, is classified
as being from a supra-ventricular origin, according to the morphological
analysis (step 56);

[0091]otherwise, the arrhythmia is not classified, insofar as the
morphological analysis does not reveal any majority or significant trend
(step 58).

[0092]FIGS. 12 and 13 display two examples of classification in accordance
with the present invention, respectively for a first patient in Sinus
Rhythm and during a SVT episode, and for a second patient in Sinus Rhythm
and during a VT episode:

[0093]FIGS. 12a and 13a respectively display the vectograms corresponding
to two reference beats, calculated as above in Sinus Rhythm for the two
respective patients;

[0094]FIG. 12b illustrates the vectogram in SVT from the first patient,
and FIG. 13b illustrates the vectogram in VT of the second patient.

[0095]FIGS. 12c, 12d and 12e respectively display, for the vectogram of
the reference beat of the first patient (vectogram from FIG. 12a): the
variations of the average angle between the unit tangent vector and the
abscissa axis; the rough curvature; and the weighted and normalized
curvature;

[0096]FIGS. 13c, 13d and 13e are homolog to FIGS. 12c, 12d and 12e, for
the vectogram of the reference beat of the second patient (vectogram from
FIG. 13a);

[0097]FIGS. 12f, 12g and 12h are homolog to FIGS. 12c, 12d and 12e, for
the vectogram of the first patient collected during a supra-ventricular
arrhythmia episode (vectogram from FIG. 12b);

[0098]FIGS. 13f, 13g and 13h are homolog to FIGS. 12f, 12g and 12h, for
the vectogram of the second patient collected during a supra-ventricular
arrhythmia episode (vectogram from FIG. 13b);

[0099]FIGS. 12i and 13i respectively indicate the average angle α
between the unit tangent vectors during an arrhythmia and in Sinus
Rhythm, compared with the decision frontier F between VT and SVT; and

[0100]FIGS. 12j and 13j indicate the correlation coefficient cc between
the vectogram curves during arrhythmia and during Sinus Rhythm, compared
with the decision frontier F between VT and SVT.

[0101]In the case of the first patient (FIG. 12), the analysis of the
unitary tangent vectors is not sufficient to conclude whether it is a SVT
(on FIG. 12i, the point α is too near from the frontier F), but the
analysis of the curvature confirms it is a SVT indeed (FIG. 12j). For the
second patient, the two criteria clearly show it is a VT (FIG. 12i and
FIG. 13j).

[0102]With reference to FIG. 14, a synopsis is shown illustrating how it
is possible to combine rhythm analysis (according to known techniques)
and morphological analysis (according to the invention) to allow the
device to make a global decision on the arrhythmia classification, and
therefore on the opportunity to apply or not a defibrillation shock to
the patient.

[0103]For the application of a dual chamber defibrillator, the
morphological analysis is notably useful when the atrioventricular
association is in 1:1, because in that case the acceleration is sudden
and the origin of this acceleration is not obvious (atrial tachycardia
(SVT)/Ventricular Tachycardia). Or again when the RR intervals are stable
and that there is no atrioventricular association (atrial fibrillation
(SVT)/Ventricular Tachycardia), because the rhythm analysis is often not
sufficient to determine for sure the origin of the arrhythmia.

[0104]For the application of a single chamber defibrillator, the
morphological analysis allows to avoid some inappropriate shocks. Indeed,
the conjunction of a situation with stable RR intervals, sudden
acceleration and absence of long cycle, considered by the rhythm analysis
as requiring a therapy, can characterize in certain situations a
Supra-Ventricular Tachycardia, which does not justify such a therapy. The
morphological analysis according to the present invention will allow
discriminating such a situation.

[0105]With reference to FIG. 14, from eight successive beats collected
during an arrhythmia (step 60), the device operates simultaneously a
rhythm analysis (step 62) and a morphological analysis (step 64 according
to the method described above with respect to FIG. 11). The rhythm
analysis operates the classification between VT, SVT or non significant
arrhythmia (no majority on the eight beats) and the morphological
analysis does the same.

[0106]Favourably, the morphological analysis is executed or taken into
account only if the rhythm analysis concludes that the arrhythmia is
originated from the ventricle (VT). In that case, the aim of the
morphological analysis is to avoid an inappropriate shock, with the
hypothesis that the sensitivity of the rhythm analysis is really equal to
one:

[0107]if the rhythm analysis concludes that the tachyarrhythmia is from
supra-ventricular origin (SVT) or undetermined (absence of majority),
then no therapy will be triggered, regardless of the result of the
morphological analysis;

[0108]if, on the contrary, the rhythm analysis concludes that the
tachyarrhythmia is from a ventricular origin (VT) and that it is
persistent, then the therapy will be triggered only if the morphological
analysis confirms the ventricular origin of this arrhythmia, when the
later is detected as well as during the persistence (e.g., twelve cycles
in the VT zone).

Second Embodiment of the Present Invention

[0109]With reference to FIGS. 15 to 18, another embodiment of the present
invention is now described. This second embodiment is also based on the
analysis and the characterization of the vectogram, but based on other
criteria than those described above with respect to the first embodiment
(unit tangent vector and weighted and normalized curvature in each
point). The considerations related to the possible method for combining
rhythm analysis and morphological analysis, described in particular in
relation with FIG. 14, are nevertheless applicable to this second
embodiment.

[0110]In this second embodiment, the orthonormal basis in which the
vectogram UnipV=f (BipV) with be represented is defined by an analysis in
principal components (an analysis named "ACP") from the Sinus Rhythm.
This ACP analysis, which is well-known in itself, can be performed for
each beat, and it allows for deducing the electrical heart axis, which is
an indicator of the general direction of propagation of the electrical
wave in the ventricles. The path with the highest dynamic is the one in
which the propagation is the greatest, with the corresponding direction
being named the "main axis". The main axis can be complemented by two
other "secondary" axes that are perpendicular with each other and with
the main axis.

[0111]In the present embodiment, the analysis will be performed in two
dimensions only (which means only one secondary axis will be considered).
Indeed, as described hereafter, the present invention technique allows
discriminating between VT and SVT from two electrodes only, which
advantageously allows the implementation of this technique in a single
chamber defibrillator.

[0112]However, despite the fact the analysis of a 2D characteristic is
sufficient to reach the expected result, in an alternate implementation
the analysis can be performed on the basis of a 3D characteristic,
collected from three electrodes.

[0113]The principal components in the ACP analysis that allows defining
the reference orthonormal basis will now be described. Let S1 and S2 be
the two signals of the A (BipV) and B (UnipV) respective channels
representing an averaged heart beat, for example, on fifteen successive
sinus beats. Each signal is constituted by N points represented in the
basis of the electrodes (A, B), (S1 (i), S2 (i)) being the
coordinates of the Ith point.

[0114]For the analysis in principal components, it is considered the N
points are approximated by an ellipse, which allows calculating:

[0115]the axis of this ellipsis constituting the ACP basis,

[0116]the length of each of them.

[0117]Those two values allow, on one hand to identify the main direction
of the ellipsis (and consequently the spreading direction of the
vectogram) and on the other hand to quantify its dimensions and its area.

[0118]Next, a study is made to determine the coordinates of these N points
in the ACP basis (P1, P2), which requires calculating a
transition matrix from the basis (A, B) to the basis (P1, P2).
The transition matrix is calculated by diagonalizing the covariance
matrix C associated to the N points. Calculating the covariance matrix is
equivalent to approximating the N points as a part of an ellipsis. By
diagonalizing this matrix, one gets:

[0119]the axis of this ellipsis, defined by the eigen vectors of C, and

[0120]the length of each of these axis, indicated by the corresponding
eigen value.

The eigen vector having the greatest eigen value thus defines the
direction of the greatest dispersion of collection of points.

[0121]Then, the eigen values (λi)i=1,2 and the eigen
vectors (V1, V2) associated to the C matrix are calculated. One
calculates the D matrix defined by:

D=P.sup..1CP

In which D is the diagonal matrix of the eigen values:

D = [ λ 1 0 0 λ 2 ] ##EQU00001##

And in which P is the transition matrix from the basis (P1, P2)
to the basis (A, B) constituted of the eigen vectors of C. Thus, the
inverse matrix of P is defined by:

P.sup.-=[P1P2]

in which Pi is the column vector i in the ACP basis (that is to say
the eigen vector associated to the λi eigen value) expressed
in the basis (A, B). By classifying the λi in the decreasing
order, the P1 vector represents the direction, in which the
collection of points is the most dispersed, and the P2 vector the
second direction. The (S1ACP, S2ACP) signal in this
new basis (P1, P2) is defined by:

[ S 1 ACP , S 2 ACP ] = P - 1 , [ S 1 S 2 ]
##EQU00002##

As indicated above, according to the present invention, the ACP basis is
calculated on the basis of the Sinus Rhythm, before projecting the Sinus
Rhythm data and the tachycardia data in this same basis.

[0122]FIG. 15 illustrates the result resulting of a basis change, for a
patient in Sinus Rhythm (loop in continuous line) with SVT episodes (loop
in dotted line). FIG. 16 is homolog to FIG. 15, for a patient in Sinus
Rhythm (loop in continuous line) with VT episodes (loop in dotted line).
By comparing FIGS. 15 and 16, it can be observed a very narrow similarity
between the Sinus Rhythm loops and the TSV loops on the direction,
orientation, shape, area and morphology, while no significant similarity
can be observed between the Sinus Rhythm loops and the VT loops.

[0123]The next step of the analysis determines a certain number of
descriptive parameters of the morphology of these loops, so as to be able
to operate, in the best conditions, a discrimination between VT and SVT
for a patient having tachycardia episodes. The analysis in principal
components performed at the previous step can notably be used to extract
the following descriptive parameters (the method to determine these
parameters will be described hereafter):

[0124]the main axis, which is the eigen vector of the covariance matrix
associated to the greatest eigen value;

[0125]the secondary axis, which is the eigen vector of the covariance
matrix associated to the second eigen value;

[0126]the dimensions of these two axis, which are the two eigen values of
the covariance matrix;

[0127]the angles between the two axis with the OX axis, extracted from the
calculations of the sines and cosines.

[0128]In order to extract from the ACP analysis descriptive mathematic
parameters of the loop morphology, each signal (Sinus Rhythm and
tachycardia) is then projected on its own basis, so as to be able to
observe the corresponding one dimension signal (which is therefore a
signal in the time domain), then compare the shapes in order to extract
the morphological parameters which differentiate the SVT from the VT.

[0129]FIG. 17 illustrates those signals in one dimension:

[0130]the referenced lines SR1 and VT1 represent the ACP
components projected on the main axis of the reference mark, respectively
for a Sinus Rhythm beat and for a Ventricular Tachycardia beat;

[0131]the referenced lines SR2 and VT2 correspond to the same
respective

[0132]ACP components on the secondary reference mark.

[0133]Once this step is performed, it is possible to extract
representative parameters, such as:

[0134]maximum height of the signals (on the two axes, main and secondary);

[0135]minimum height of the signals (on the two axes, main and secondary);

[0136]width of the signals (on the two axes, main and secondary).

From these morphological parameters, the algorithm then calculates
correlation coefficients between, on one hand the Sinus Rhythm and
Supra-Ventricular Tachycardia signals and, on the other hand, those
coefficients being calculated on the main and the secondary channels. The
average square error compared to the Sinus Rhythm is also calculated, for
the Supra-Ventricular Tachycardia beats and for the VT beats. The
distribution obtained in the two cases of tachycardia is illustrated,
with reference to FIG. 18, where are displayed: [0137]in abscissa, the
ratio of the eigen values on the main channel of the Sinus Rhythm and of
the SVT or of the VT, and

[0138]in ordinate, the correlation coefficient between SR and SVT or
between SR and VT.

[0139]This distribution shows that the data collected in the case of a VT
and in the case of a Supra-Ventricular Tachycardia are very well
separated and that it is thus possible to operate a classification of the
tachycardias and a relevant discrimination by implementing, for example,
a linear classifier or a neural classifier, in accordance with a process
that will be described hereafter.

[0140]The descriptive parameters of the 2D loop morphologies that can be
used to operate this classification of the tachycardias will now be
described in more detail. From the patient's Sinus Rhythm EGM:

[0141]the first eigen value λ1,SR and the second eigen value
λ2,SR of the analysis calculation in principal components;

[0142]the θSR angle between the first main axis of the beat and
the first recording channel;

[0143]the R1,SR ratio between the depolarisation complex maximum and
minimum amplitudes on the first main channel; and

[0144]the R2,SR ratio between the depolarisation complex maximum and
minimum amplitudes on the second channel.

[0145]In the same way, for tachycardia beats (VT or SVT) it is possible to
obtain the following parameters:

[0146]the first eigen value λ1,TR and the second eigen value
λ2,TR from the analysis calculation in principal components;

[0147]the θTR angle between the first main axis of the beat and
the first recording channel;

[0148]the R1,TR ratio between the depolarisation complex maximum and
minimum amplitudes on the first main channel; and

[0149]the R2,TR ratio between the depolarisation complex maximum and
minimum amplitudes on the second channel.

[0150]For the comparison of the sinus beat and of the tachycardia beat the
following representative parameters can be used:

[0151]maximum of correlation M1 on the first main channel between the
line of Sinus Rhythm beat and the line of the first tachycardia beat;

[0152]maximum of correlation M2 between the lines on the second main
channel; and/or

[0153]mean squared error MSE between the two beats on the first main
channel.

[0154]From these parameters, it is possible to calculate various
representative expressions designated below as D1 to D5. The D1
expression below, which is the ratio of the first and second eigen value
calculation of principal components, reflects the shape of the vectogram
loop associated with the beat, so the form report between sinus beats
strongly and tachycardia beat:

D 1 = ( λ 1 / λ 2 ) SR ( λ 1 /
λ 2 ) TR ##EQU00003##

[0155]The term D2 below reflects the ratio between the fraction of the
information contained on the main track for the sinus beat and the one
contained on the main track for the tachycardia beat
(λ1/(λ1+λ2) reflecting the proportion of
information expressed by the main track in relation to the total
information available on both channels):

[0156]If we designate θ as the angle formed by the main axis with
the first track recording, the D3 expression below reflects the
directions of propagation of the beat in Sinus Rhythm and in tachycardia:

D 3 = θ SR θ TR ##EQU00005##

[0157]Finally, the D4 and D5 expressions below highlight the differences
of the traces on the first main track and the second main track of the
two Sinus Rhythm and tachycardia beats

D 4 = R 1 , SR R 1 , TR , D 5 = R 2 , SR R 2 , TR
##EQU00006##

[0158]The discrimination between VT and Supra-Ventricular Tachycardia can
then be performed by various types of classifiers, in particular by a
linear classifier or a neural classifier. A first mode of implementation
builds a linear classifier in the 3D space formed by such three
descriptors MSE, M1 and D1 (this method also being applicable to the use
of other descriptors). Such a classifier is characterized by the equation
of the plane separating in this space the two families of arrhythmias, VT
and SVT.

[0159]A robust plane separator can be obtained by minimization of least
square of the distance of each sample to the plane. The equation of the
plane, characterized by its orthogonal vector A is:

[0160]The matrix X is the matrix containing for each of arrhythmias the
value of the three descriptors in columns, and a fourth column of 1. This
matrix has the following structure, assuming that there is a database of
patients with from 1 to N arrhythmias:

[0161]The matrix Y is the vector consisting of -1 when the point
corresponds to a Supra-Ventricular Tachycardia and of +1 if the point
corresponds to a VT.

[0162]O is the matrix that contains the new value of the descriptors in
columns and a fourth column of 1 to classify an arrhythmia and Z is the
matrix defined by:

Z=AtO.

If Z is negative, the arrhythmia is classified as a SVT; if z is positive
the arrhythmia is classified as a VT.

[0163]Alternatively, in order to simplify calculations and reduce the
workload of the processor, it is possible to apply most of the principles
described in the above without using principal component analysis. Thus,
reports R1,SR and R2,SR, respectively between the maximum and
minimum amplitude of the depolarization complex on the axes BipV and
UnipV from the patient's Sinus Rhythm EGM, as well as the ratio
R1,TR and R2,TR for the tachycardia beats can be determined
without using principal component analysis. In the same way may the
maximum of correlation M1 and M2 between the Sinus Rhythm beat route and
the tachycardia beat route be determined, respectively of the BipV and
UnipV axes. Based on the values so determined, then it is possible to
deduce D4 and D5 values, for further analysis on the basis of these
descriptors, as described in the preceding paragraphs.

[0164]The database is scalable and is continuously filled in the device,
each arrhythmia being added or taking the place of an arrhythmia of the
database. Also, the device, implant or programmer, recalculates on a
regular basis the matrix A.

[0165]The device may include complementary means to post check the
classification of the arrhythmia, for example:

[0166]If the device detects a VT by the method of linear classification,
it notifies the patient by a beep. If the VT disappears, the
classification was erroneous;

[0167]If the device detects a SVT which becomes a Ventricular
Fibrillation, the classification was erroneous; and

[0168]If the device makes a misclassification of an arrhythmia, an ECG
record like a Holter, allows to detect it, the physician indicates it by
telemetry to the defibrillator.

[0169]The device having proved a misclassification of an arrhythmia can
either:

[0170]add the arrhythmia to the data base, replace an arrhythmia of the
same type in the data base and then recalculate the matrix A.

[0171]Another mode of realization can, alternatively, implement a neural
classifier, which notably allows operating by means of an adaptive
network, instead of a pure mathematical calculation. This classifier is
constructed in the 3D space, for example, using the three descriptors
MSE, M1 and D1 (this method also being applicable to the use of other
descriptors).

[0172]Such a classifier is characterized by the equation of the plane
separating in that space the two arrhythmia families, VT and SVT:

y=f(WTφ)

y=+1 if WTφ≧0;

y=-1 if Wφ>0

W being the vector constituted of weights applied to each descriptor;φ
being the vector including for an arrhythmia in columns the value of the
three descriptors and the bias 1 (MSE, M1, D1, 1);y being the predictor:
if y is negative the arrhythmia is classified as a Supra-Ventricular
Tachycardia, if y is positive the arrhythmia is classified as a VT.

[0173]The value of W is determined by the deterministic gradient algorithm
by applying the following rule:

. W is initialized;The learning is performed on a set of arrhythmias
previously classified and confirmed:

[0174]if the prediction is good W is not modified

[0175]if for a Supra-Ventricular Tachycardia φn the prediction is
wrong, W is subtracted from the value φn;

[0176]if for a Ventricular Tachycardia φn the prediction is
wrong, W is added to φn

[0177]The data base is scalable and is continuously filled in the device,
each arrhythmia either being added or replacing an arrhythmia in the data
base. Furthermore, the device, implant or programmer, redoes on a regular
basis the learning of W.

[0178]Here again, the device can include complementary means to post
verify the classification of the arrhythmia, of the same type as those
exposed above, leading to new learning of Won a regular basis in the case
of classification errors.

[0179]One skilled in the art will appreciate that the present invention
may be practiced by other than the embodiments described herein, which
are provided for purposes of illustration and not of limitation.

Patent applications by Christine Henry, Paris FR

Patent applications by Remi Dubois, Paris FR

Patent applications by Renzo Dal Molin, Chatillon FR

Patent applications in class Tachycardia or fibrillation detected

Patent applications in all subclasses Tachycardia or fibrillation detected